Climate-Driven Stochastic Modelling of Crimean-Congo Haemorrhagic Fever Transmission in Uganda
DOI:
https://doi.org/10.63746/njtd.v23i2.4823Keywords:
Crimean–Congo haemorrhagic fever, Stochastic differential equations, Climate-driven transmission, Hyalomma ticks, Basic reproduction number, UgandaAbstract
Crimean-Congo haemorrhagic fever (CCHF) is a climate-sensitive tick-borne zoonosis that remains a significant public health concern in Uganda, where temperature and vapour pressure deficit influence tick ecology and consequently disease transmission. This study aimed to develop and analyse a climate-driven stochastic model for CCHF transmission among ticks, livestock, and humans under environmental variability in Uganda. A compartmental transmission model was formulated and extended into a stochastic differential equation framework by incorporating multiplicative environmental noise. Climate-dependent tick recruitment, development, and mortality were parameterized using district-level temperature and vapour pressure deficit data to capture spatial heterogeneity in transmission risk. Theoretical analyses based on the next-generation matrix, Lyapunov methods, and stochastic stability theory were used to derive the basic reproduction number and establish positivity, boundedness, equilibrium stability, extinction conditions, and the existence of a stationary distribution and ergodicity. Numerical simulations were performed using the Milstein scheme over a three-year period to compare deterministic and stochastic dynamics under endemic and sub-threshold transmission scenarios, while district-level climate data were used to generate spatial risk maps and evaluate model performance against reported CCHF outbreaks. The model reproduced seasonal transmission patterns, predicted rapid disease extinction under sub-threshold conditions, and persistent endemic dynamics when Ro > 1. Spatial analyses revealed substantial geographical variation in transmission potential, and model validation achieved a classification accuracy of 79.6% against reported outbreak districts. The proposed framework provides a practical tool for climate-informed CCHF surveillance, spatial risk assessment, early warning, and prioritization of targeted intervention strategies in Uganda.
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